As background, unexpected machine downtime is still one of the major issues impacting machining productivity in industry. For example, every minute of downtime in an automotive manufacturing plant could be quite costly, as the breakdown of one machine may result in the halt of the entire production line in a manufacturing facility. As machine tools become more complex and sophisticated, the reliability of the machining equipment becomes more crucial. Most machine maintenance today is either purely reactive (reactive maintenance) or blindly proactive (preventive maintenance), both of which could be extremely wasteful.
Predictive maintenance focuses on failure prediction in order to prevent failures in advance, and offers sufficient information to improve overall maintenance scheduling. For decades, researchers and practitioners have been trying to develop and deploy prognostics technologies with ad hoc and trial-and-error approaches. These efforts have resulted in limited success, due to the fact that a systematic approach in deploying the right prognostics models for the right applications has yet to be developed.
Before the deployment of the right prognostics models, several factors for complex systems, such as stability properties and modeling assumptions and operating conditions, must be taken into consideration. Stability properties and modeling assumptions are important for building physics models for a controller or machine process. Operating conditions, such as shaft speed, load, feed rate and cutting materials, are also important factors for prognostic models since the degradation patterns of the machine may be distinct under different operating conditions. A system's full range of operating states may be decomposed into four overlapping operating conditions based on two principle parameters, which may include shaft speed, load, feed rate, and cutting materials, etc. Under a certain operating condition (e.g. low speed cutting of a soft material), the degradation pattern of the machine may be a slow and stationary process; while under another operating condition (e.g. high speed cutting of a hard material), the degradation pattern may show non-stationary characteristics with a faster degradation rate towards failure. It may be difficult for an individual prognostic model to meet the accuracy requirements for prediction when the machine operating condition changes.
Many system components can undergo a long degradation process before catastrophic failures occur. If a certain operating condition is continuously examined, the degradation status of the component will change over time. Performance indices (e.g., “1” meaning normal, and “0” meaning unacceptable) may be stable in the range of 0.9 to 1.0 at the beginning. As the initial faults develop over time, a degradation trend appears in the performance indices. At the final stage of the degradation, the trend of the performance indices drops quickly towards 0. An individual model cannot always meet the accuracy requirements for prediction when the machine degradation status changes overtime. Some prediction models are only appropriate for specific degradation patterns. These models may fail to learn and predict for aliasing degradation patterns accurately. A method which incorporates multiple prediction models may solve this issue, while the challenge still remains in how to autonomously shift among these multiple models to improve the prediction accuracy.
Therefore, novel methods are disclosed to address the challenges of performance degradation identification, adaptive prediction model selection and performance index generation for robust prognostics. These methods leverage the machine prognostics strategy both in autonomy and accuracy.